In code-division multiple access (CDMA) systems, multiple access interference (MAI) is a factor that contributes to a limitation in system capacity and performance. In an attempt to reduce the effect of this factor, one can employ some form of multi-user detection (MUD) algorithm. The basis of a typical MUD algorithm is the application of information known about other users to improve detection of each individual user. Of particular interest is the class of MUD algorithms known as subtractive interference cancellation detectors. The fundamental principle behind these detectors is that an estimate is made of each individual user's contribution to the total MAI and then subtracted out from the received signal such that the MAI affecting each individual user is reduced.
If the estimation and subtraction of the MAI occurs in parallel for each user, the resulting detector is known as a parallel interference cancellation (PIC) detector. Typically, the detection process is carried out in an iterative manner, where the data decisions of the previous iteration are used as the basis for the next iteration's MAI estimates. In general, the reliability of these data decisions improves as the number of iterations increases. In a conventional PIC detector, each cancellation iteration involves an attempt to cancel out all or a portion of the MAI. For each individual user, this is accomplished by directly subtracting out the MAI estimates for all the other users.
As the number of users in a particular communication environment increases, and the data rates increase, the complexity associated with parallel interference cancellation significantly increases. As such, there is a need for a way to provide parallel interference cancellation in an effective and efficient manner, which reduces the complexities of previous parallel interference cancellation architectures.